{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:10:55Z","timestamp":1783627855777,"version":"3.55.0"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,3]],"date-time":"2018-02-03T00:00:00Z","timestamp":1517616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Microfluidic devices are an emerging platform for a variety of experiments involving bacterial cell culture, and has advantages including cost and convenience. One inevitable step during bacterial cell culture is the measurement of cell concentration in the channel. The optical density measurement technique is generally used for bacterial growth estimation, but it is not applicable to microfluidic devices due to the small sample volumes in microfluidics. Alternately, cell counting or colony-forming unit methods may be applied, but these do not work in situ; nor do these methods show measurement results immediately. To this end, we present a new vision-based method to estimate the growth level of the bacteria in microfluidic channels. We use Fast Fourier transform (FFT) to detect the frequency level change of the microscopic image, focusing on the fact that the microscopic image becomes rough as the number of cells in the field of view increases, adding high frequencies to the spectrum of the image. Two types of microfluidic devices are used to culture bacteria in liquid and agar gel medium, and time-lapsed images are captured. The images obtained are analyzed using FFT, resulting in an increase in high-frequency noise proportional to the time passed. Furthermore, we apply the developed method in the microfluidic antibiotics susceptibility test by recognizing the regional concentration change of the bacteria that are cultured in the antibiotics gradient. Finally, a deep learning-based data regression is performed on the data obtained by the proposed vision-based method for robust reporting of data.<\/jats:p>","DOI":"10.3390\/s18020447","type":"journal-article","created":{"date-parts":[[2018,2,5]],"date-time":"2018-02-05T04:29:42Z","timestamp":1517804982000},"page":"447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Visual Estimation of Bacterial Growth Level in Microfluidic Culture Systems"],"prefix":"10.3390","volume":"18","author":[{"given":"Kyukwang","family":"Kim","sequence":"first","affiliation":[{"name":"Robotics Program, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0661-4583","authenticated-orcid":false,"given":"Seunggyu","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6690-5775","authenticated-orcid":false,"given":"Jessie","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea"},{"name":"KAIST Institute for Health Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3914","DOI":"10.1039\/c2lc40294g","article-title":"Microfabricated ratchet structure integrated concentrator arrays for synthetic bacterial cell-to-cell communication assays","volume":"12","author":"Park","year":"2012","journal-title":"Lab Chip"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"455","DOI":"10.3390\/antibiotics4040455","article-title":"Miniaturized Antimicrobial Susceptibility Test by Combining Concentration Gradient Generation and Rapid Cell Culturing","volume":"4","author":"Kim","year":"2015","journal-title":"Antibiotics"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10569","DOI":"10.3390\/s150510569","article-title":"Sensor Node for Remote Monitoring of Waterborne Disease-Causing Bacteria","volume":"15","author":"Kim","year":"2015","journal-title":"Sensors"},{"key":"ref_4","first-page":"220","article-title":"Beyond growth rate 0.6: Corynebacterium glutamicumcultivated in highly diluted environments","volume":"110","author":"Paczia","year":"2012","journal-title":"Biotechnol. Bioeng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1128\/aem.56.4.1004-1011.1990","article-title":"Comparison of growth, acetate production, and acetate inhibition of Escherichia coli strains in batch and fed-batch fermentations","volume":"56","author":"Luli","year":"1990","journal-title":"Appl. Environ. Microbiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1016\/j.cub.2010.04.045","article-title":"Robust Growth of Escherichia coli","volume":"20","author":"Wang","year":"2010","journal-title":"Curr. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3296","DOI":"10.1039\/c0lc00154f","article-title":"In situ monitoring of antibiotic susceptibility of bacterial biofilms in a microfluidic device","volume":"10","author":"Kim","year":"2010","journal-title":"Lab Chip"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4269","DOI":"10.1039\/c2lc40391a","article-title":"Portable self-contained cultures for phage and bacteria made of paper and tape","volume":"12","author":"Wu","year":"2012","journal-title":"Lab Chip"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1038\/s41598-017-01454-4","article-title":"High throughput single cell counting in droplet-based microfluidics","volume":"7","author":"Lu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kim, K., Choi, D., Lim, H., Kim, H., and Jeon, J. (2016). Vision Marker-Based In Situ Examination of Bacterial Growth in Liquid Culture Media. Sensors, 16.","DOI":"10.3390\/s16122179"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kim, K., Hyun, J., and Jeon, J. (2017). Light Emitting Marker for Robust Vision-Based On-The-Spot Bacterial Growth Detection. Sensors, 17.","DOI":"10.3390\/s17061459"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Toyoura, M., Aruga, H., and Turk, M. (2013, January 21\u201323). Detecting Markers in Blurred and Defocused Images. Proceedings of the 2013 International Conference on Cyberworlds (CW 2013), Yokohama, Japan.","DOI":"10.1109\/CW.2013.58"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1038\/nprot.2012.051","article-title":"Microfluidic assay for simultaneous culture of multiple cell types on surfaces or within hydrogels","volume":"7","author":"Shin","year":"2012","journal-title":"Nat. Protoc."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wijnen, B., Hunt, E., Anzalone, G., and Pearce, J. (2014). Open-Source Syringe Pump Library. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0107216"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Farahat, W.A., Wood, L.B., Zervantonakis, I.K., Schor, A., Ong, S., Neal, D., Kamm, R.D., and Asada, H.H. (2012). Ensemble analysis of angiogenic growth in three-dimensional microfluidic cell cultures. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0037333"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1089\/ten.tea.2013.0744","article-title":"Abluminal Stimulation of Sphingosine 1-Phosphate Receptors 1 and 3 Promotes and Stabilizes Endothelial Sprout Formation","volume":"21","author":"Das","year":"2015","journal-title":"Tissue Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"A Tutorial on the Cross-Entropy Method","volume":"134","author":"Kroese","year":"2005","journal-title":"Ann. Oper. Res."},{"key":"ref_18","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2017, December 23). Caffe: Convolutional Architecture for Fast Feature Embedding. Available online: https:\/\/arxiv.org\/abs\/1408.5093."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shekarabi, M., Hajikhani, B., Salimi Chirani, A., Fazeli, M., and Goudarzi, M. (2017). Molecular characterization of vancomycin-resistant Staphylococcus aureus strains isolated from clinical samples: A three year study in Tehran, Iran. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0183607"},{"key":"ref_20","unstructured":"Simonyan, K., and Zisserman, A. (2017, December 23). Very Deep Convolutional Networks for Large-Scale Image Recognition. Available online: https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"ref_21","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2017, December 23). Going Deeper with Convolutions. Available online: https:\/\/arxiv.org\/abs\/1409.4842."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_23","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2017, December 23). Fully Convolutional Networks for Semantic Segmentation. Available online: https:\/\/arxiv.org\/abs\/1411.4038."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2017, December 23). You Only Look Once: Unified, Real-Time Object Detection. Available online: https:\/\/arxiv.org\/abs\/1506.02640.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_25","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T. (2017, December 23). One-shot Learning with Memory-Augmented Neural Networks. Available online: https:\/\/arxiv.org\/abs\/1605.06065."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2891","DOI":"10.1039\/C6LC00332J","article-title":"Lab on a stick: Multi-analyte cellular assays in a microfluidic dipstick","volume":"16","author":"Reis","year":"2016","journal-title":"Lab Chip"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4087","DOI":"10.1039\/c1lc20670b","article-title":"A scalable microfluidic chip for bacterial suspension culture","volume":"11","author":"Gan","year":"2011","journal-title":"Lab Chip"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/2\/447\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:53:40Z","timestamp":1760194420000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/2\/447"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,3]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["s18020447"],"URL":"https:\/\/doi.org\/10.3390\/s18020447","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,3]]}}}